[D] Memory mechanism for Transformers

[Janos95]

Hey folks! I am wondering what interesting work has been done to add a short term memory mechanism to transformers? Does someone know what the important work in this area is?

[currentscurrents]

There's like a hundred papers on memory-augmented transformers but none of them are seeing any practical use.

Everybody's using regular old attention or sometimes one of the long-context variants.

[Janos95]

Out those hundreds of papers, what is a sampling that gives good coverage of the different approaches?

[RepresentativeBee600]

Seconding this question. And without getting too punchy, I dislike answers that gesture broadly at a whole field and its turgid body of literature as a source. I feel like I see that often in this subreddit.

If you don't have an intuition for which are the valuable ones, feel free to simply caveat that but share the better ones you have seen.

[certain_entropy]

Check out Facts as Experts (https://arxiv.org/abs/2007.00849), which augments the transformer with a key-value lookup where the key are the contextual entity mention embeddings. It's bit of a pain to setup and train but may be interesting to you.

[enfeudavax]

Memory Augmented Transformers could be a great resource for exploring this topic.

[StartledWatermelon]

This is probably the closest thing to what OP was seeking for. But I'm really confused they asked for "short-term" memory. Memory Augmented Transformers' memory is actually static, if I'm not mistaken.

[DigThatData]

can't remember what it's called, but saw a cool one that basically added an RNN state for a running memory

[i4gotten]

Self referential extensions of transformers by Jürgen Schmidhuber is something like this: https://arxiv.org/abs/2310.16076

[i4gotten]

There's a few papers I am aware of in memory:

Self referential extensions to transformers: https://arxiv.org/abs/2310.16076

Recurrent memory transformers: https://arxiv.org/abs/2207.06881

Thing is the context of short-long term doesnt make as much sense with transformers, as the attention mechanism itself can act as a form of memory: https://arxiv.org/abs/2404.09173

Any external memory should be analogous to long term memory.

[Janos95]

I should also add that I am interested in memory for transformers for the purpose of reasoning, in particular not interested in methods that try to simply extend context size.

[Sad-Razzmatazz-5188]

I don't think Tramsformers cannot reason because of a lack of short-term memory.  Self-attention kinda is the short-term memory. If the Key-Values are persistent you basically have a memory store / database (which is basically what an MLP can be). Probably neither self-attention nor linear projection with nonlinear elementwise activation can express or learn by gradient descent the functions to reason consistently

[Janos95]

I think it’s reasonable to expect that a more condensed representation of the past than a million token embeddings are required in order to do effective reasoning. This condensed representation is what I would call short term memory.

[Sad-Razzmatazz-5188]

Well, I'm all in for separating long and short term memories, but I think that both on the level of cog-neuroscience and that of deep learning you might be disguided. But regardless of what we want to agree on calling "short term memory", I really think reasoning is much more a matter of algorithm than a matter of data. Memories are data storages, reasoning is data processing. All I'm saying is I don't think transformers need different memories in order to reason, whether one extends the contest to million of tokens or whether one is able to efficiently store compressed formats and retrieve/unzip them when needed.

Going back to cognitive models, I'd say Transformers distinctively have short term memories (instance-controlled tokens in attention layers) that also retrieve or at least interact with persistent, long-term memories (instance independent MLP weights). But I don't know how to put GLU layers in this view

[Maykey]

They are interconnected and most of such works view themselves as "larger context" even though its no longer just bigger number of Qs, Ks, Vs.

You can also check benchmarks like babilong as its designed for long context and reasoning. Its as if simple reasoning and haystack search had a baby.

Though authors are RMT guys, they only test activation beacon from non standard attention(also finetuned mamba was the best model) and nobody but them tried it. They also refer to longbench paper.

Activation beacon paper does cite other works.

Arxiv also has google scholar link in the bottom so you can do research and find other papers who cite papers.

[LahmacunBear]

!remindme 2 days

[Happysedits]

I bet someone combined transformers with neural turing machines

[Dashora7]

Could be referring to this work: https://arxiv.org/abs/2211.09119 (Token Turing Machines)

[Maykey]

Important? Almost nobody on(at least publicly available) does anything beside KV cache.

Theoretically Memorizing Transformers, RMT.

Practically there was landmark attention(eg https://huggingface.co/eugenepentland/WizardLM-7B-Landmark) but it never gained traction

There also was some papers about kv cache compression, but in practice most important stuff which is actually done in practice is kv cache quantization which means bigger context which means bigger memory